mirror of
https://github.com/Doctorado-ML/bayesclass.git
synced 2025-08-17 16:45:54 +00:00
123 lines
3.0 KiB
Python
123 lines
3.0 KiB
Python
import pytest
|
|
import numpy as np
|
|
from sklearn.datasets import load_iris
|
|
from sklearn.preprocessing import KBinsDiscretizer
|
|
from matplotlib.testing.decorators import image_comparison
|
|
from matplotlib.testing.conftest import mpl_test_settings
|
|
|
|
|
|
from bayesclass.clfs import TAN
|
|
from .._version import __version__
|
|
|
|
|
|
@pytest.fixture
|
|
def data():
|
|
X, y = load_iris(return_X_y=True)
|
|
enc = KBinsDiscretizer(encode="ordinal")
|
|
return enc.fit_transform(X), y
|
|
|
|
|
|
@pytest.fixture
|
|
def clf():
|
|
return TAN()
|
|
|
|
|
|
def test_TAN_default_hyperparameters(data, clf):
|
|
# Test default values of hyperparameters
|
|
assert not clf.show_progress
|
|
assert clf.random_state is None
|
|
clf = TAN(show_progress=True, random_state=17)
|
|
assert clf.show_progress
|
|
assert clf.random_state == 17
|
|
clf.fit(*data)
|
|
assert clf.head_ == 0
|
|
assert clf.class_name_ == "class"
|
|
assert clf.feature_names_in_ == [
|
|
"feature_0",
|
|
"feature_1",
|
|
"feature_2",
|
|
"feature_3",
|
|
]
|
|
|
|
|
|
def test_TAN_version(clf):
|
|
"""Check TAN version."""
|
|
assert __version__ == clf.version()
|
|
|
|
|
|
def test_TAN_nodes_edges(clf, data):
|
|
assert clf.nodes_edges() == (0, 0)
|
|
clf = TAN(random_state=17)
|
|
clf.fit(*data, head="random")
|
|
assert clf.nodes_leaves() == (5, 7)
|
|
|
|
|
|
def test_TAN_states(clf, data):
|
|
assert clf.states_ == 0
|
|
clf = TAN(random_state=17)
|
|
clf.fit(*data)
|
|
assert clf.states_ == 23
|
|
assert clf.depth_ == clf.states_
|
|
|
|
|
|
def test_TAN_random_head(data):
|
|
clf = TAN(random_state=17)
|
|
clf.fit(*data, head="random")
|
|
assert clf.head_ == 3
|
|
|
|
|
|
def test_TAN_classifier(data, clf):
|
|
clf.fit(*data)
|
|
attribs = [
|
|
"classes_",
|
|
"X_",
|
|
"y_",
|
|
"head_",
|
|
"feature_names_in_",
|
|
"class_name_",
|
|
]
|
|
for attr in attribs:
|
|
assert hasattr(clf, attr)
|
|
X = data[0]
|
|
y = data[1]
|
|
y_pred = clf.predict(X)
|
|
assert y_pred.shape == (X.shape[0],)
|
|
assert sum(y == y_pred) == 147
|
|
|
|
|
|
@image_comparison(
|
|
baseline_images=["line_dashes_TAN"], remove_text=True, extensions=["png"]
|
|
)
|
|
def test_TAN_plot(data, clf):
|
|
# mpl_test_settings will automatically clean these internal side effects
|
|
mpl_test_settings
|
|
dataset = load_iris(as_frame=True)
|
|
clf.fit(*data, features=dataset["feature_names"], head=0)
|
|
clf.plot("TAN Iris head=0")
|
|
|
|
|
|
def test_TAN_wrong_num_features(data, clf):
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="Number of features does not match the number of columns in X",
|
|
):
|
|
clf.fit(*data, features=["feature_1", "feature_2"])
|
|
|
|
|
|
def test_TAN_wrong_hyperparam(data, clf):
|
|
with pytest.raises(ValueError, match="Unexpected argument: wrong_param"):
|
|
clf.fit(*data, wrong_param="wrong_param")
|
|
|
|
|
|
def test_TAN_head_out_of_range(data, clf):
|
|
with pytest.raises(ValueError, match="Head index out of range"):
|
|
clf.fit(*data, head=4)
|
|
|
|
|
|
def test_TAN_error_size_predict(data, clf):
|
|
X, y = data
|
|
clf.fit(X, y)
|
|
with pytest.raises(ValueError):
|
|
X_diff_size = np.ones((10, X.shape[1] + 1))
|
|
clf.predict(X_diff_size)
|